Fix progress bar display to correctly handle iterable dataset and max_steps during training

src/lightning/pytorch/callbacks/progress/progress_bar.py

@@ -85,6 +85,9 @@ def total_train_batches(self) -> Union[int, float]:
         dataloader is of infinite size.
 
         """
+        if self.trainer.max_epochs == -1 and self.trainer.max_steps is not None and self.trainer.max_steps > 0:
+            remaining_steps = self.trainer.max_steps - self.trainer.global_step
+            return min(self.trainer.num_training_batches, remaining_steps)
         return self.trainer.num_training_batches
 
     @property

tests/tests_pytorch/loops/test_training_loop.py

@@ -11,11 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import itertools
 import logging
 from unittest.mock import Mock
 
 import pytest
 import torch
+from torch.utils.data import DataLoader
 
 from lightning.pytorch import Trainer, seed_everything
 from lightning.pytorch.demos.boring_classes import BoringModel
@@ -206,3 +208,70 @@ def test_should_stop_early_stopping_conditions_met(
 
     assert (message in caplog.text) is raise_debug_msg
     assert trainer.fit_loop._can_stop_early is early_stop
+
+
+@pytest.mark.parametrize("max_steps", [7, 20])
+def test_tqdm_total_steps_with_iterator_no_length(tmp_path, max_steps):
+    """Test trainer with infinite iterator (no __len__)"""
+
+    batch_size = 4
+    model = BoringModel()
+
+    # Infinite generator (no __len__)
+    # NOTE: 32 for BoringModel
+    infinite_iter = (torch.randn(batch_size, 32, dtype=torch.float32) for _ in itertools.count(0))
+
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        max_steps=max_steps,
+        max_epochs=-1,
+        limit_val_batches=0,
+        enable_progress_bar=True,
+        enable_model_summary=False,
+        accelerator="cpu",
+    )
+
+    # Override train_dataloader with infinite iterator
+    model.train_dataloader = lambda: infinite_iter
+    trainer.fit(model)
+
+    # tqdm total steps should equal max_steps for iterator with no length
+    assert trainer.estimated_stepping_batches == max_steps
+
+
+@pytest.mark.parametrize("max_steps", [10, 15])
+def test_progress_bar_steps(tmp_path, max_steps):
+    batch_size = 4
+
+    model = BoringModel()
+    # Create dataloader here, outside the model
+    # NOTE: 32 for boring model
+    x = torch.randn(100, 32)
+
+    class SingleTensorDataset(torch.utils.data.IterableDataset):
+        def __init__(self, data):
+            super().__init__()
+            self.data = data
+
+        def __iter__(self):
+            yield from self.data  # yield just a tensor, not a tuple
+
+    dataset = SingleTensorDataset(x)
+    dataloader = DataLoader(dataset, batch_size=batch_size)
+
+    # Patch model's train_dataloader method to return this dataloader
+    model.train_dataloader = lambda: dataloader
+
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        max_steps=max_steps,
+        max_epochs=-1,
+        limit_val_batches=0,
+        enable_progress_bar=True,
+        enable_model_summary=False,
+        accelerator="cpu",
+    )
+    trainer.fit(model)
+
+    # tqdm total steps should equal max_steps for iterator with no length
+    assert trainer.estimated_stepping_batches == max_steps